Percentage of free to total <scp>PSA</scp> as a biomarker of survival in metastatic castration‐resistant prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To analyse whether the percentage of free to total prostate-specific antigen (%fPSA) is a prognostic biomarker in metastatic castration-resistant prostate cancer (mCRPC), as novel studies suggest an elevated %fPSA is associated with adverse oncological outcomes for men with biochemical recurrence of prostate cancer. PATIENTS AND METHODS: A biobank prospectively collated at mCRPC diagnosis was analysed for %fPSA. Clinicopathological characteristics, systemic therapies and survival outcomes were recorded. Patients were stratified by a %fPSA cut-off of 15%. Cox proportional hazard models evaluated whether %fPSA was associated with overall survival (OS) and cancer-specific survival (CSS) across the cohort and by treatment. RESULTS: A total of 254 patients analysed with newly diagnosed mCRPC: 161 (63%) men having a %fPSA ≥15%. The median follow-up was 25.6 months. The median cohort OS and CSS was 39.6 and 43.8 months, respectively. Patients with a %fPSA ≥15% had lower median PSA level (31.30 vs 50.80 ng/mL; P = 0.007) and otherwise comparable clinicopathological and treatment profiles to men with a %fPSA <15%. Adjusting for PSA and on multivariable analysis, a %fPSA ≥15% was associated with shorter OS (multivariable hazard ratio [HR] 1.56, 95% confidence interval [CI] 1.02-2.40; P = 0.039). Among men treated with docetaxel, a %fPSA ≥15% was associated with worse OS (HR 1.84, 95% CI 1.03-3.26; P = 0.038) and CSS. Conversely, %fPSA was not associated with outcomes for men receiving androgen receptor pathway inhibitors (abiraterone acetate or enzalutamide). CONCLUSION: An elevated %fPSA appears to be an adverse prognostic biomarker. Findings are consistent with biochemical recurrence studies, suggesting a biological basis. Validation and mechanistic studies are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".